Flood forecasting with machine Learning, data Assimilation and Semi-pHysical modeling

Yann Visserot; Guillaume Artigue; Pierre-Alain Ayral; Audrey Bornancin-Plantier; Anne Johannet; Bruno Janet; Arthur Marchandise; Caroline Wittwer

doi:10.1007/s10489-010-0210-y

Poster communications

Flood forecasting with machine Learning, data Assimilation and Semi-pHysical modeling

Yann Visserot ¹ Guillaume Artigue ¹ Pierre-Alain Ayral ¹ Audrey Bornancin-Plantier ¹ Anne Johannet ¹ Bruno Janet ² Arthur Marchandise ² Caroline Wittwer ²

1 LGEI - Laboratoire de Génie de l'Environnement Industriel

2 SCHAPI - Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations

Abstract : ANR FLASH project (2009-2013) intends to capitalize on the advantages of machine learning methods in order to provide tools for real-time flash floods forecasting. In a first step, water level forecasts were provided based on rain estimation of rainfalls, leading to the design of a demonstrating software. In a second step, weather RADAR measurements will be taken in advantage, as for rainfall estimation than for directly inputs reflectivity to the model. Comparison between the 3-type of inputs (rain gauge rainfall, RADAR rainfall, COMEPHORE reanalysis) will be assessed.

Document type :

Poster communications

Domain :

Engineering Sciences [physics]

Complete list of metadata

https://hal.mines-ales.fr/hal-03341863
Contributor : Administrateur Imt - Mines Alès <>
Submitted on : Monday, September 13, 2021 - 8:39:08 AM
Last modification on : Thursday, September 16, 2021 - 3:08:25 AM

Flood forecasting with machine Learning, data Assimilation and Semi-pHysical modeling

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Flood forecasting with machine Learning, data Assimilation and Semi-pHysical modeling

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